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Python: [BREAKING] Main to core (#983)
* removed pydantic from types * fix assistants client * Remove Pydantic usage from workflow code. * updated lock and test fixes * moved main to core, and setup meta package * updated versions * updated lock * fixed agents dependency * added retry to merge tests --------- Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
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# Get Started with Microsoft Agent Framework
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Highlights
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- Flexible Agent Framework: build, orchestrate, and deploy AI agents and multi-agent systems
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- Multi-Agent Orchestration: Group chat, sequential, concurrent, and handoff patterns
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- Plugin Ecosystem: Extend with native functions, OpenAPI, Model Context Protocol (MCP), and more
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- LLM Support: OpenAI, Azure OpenAI, Azure AI, and more
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- Runtime Support: In-process and distributed agent execution
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- Multimodal: Text, vision, and function calling
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- Cross-Platform: .NET and Python implementations
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## Quick Install
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```bash
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pip install agent-framework[all]
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# Optional: Add Azure AI integration
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pip install agent-framework-azure-ai
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# Optional: Both
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pip install agent-framework-azure-ai agent-framework-copilotstudio
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```
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Supported Platforms:
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- Python: 3.10+
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- OS: Windows, macOS, Linux
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## 1. Setup API Keys
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Set as environment variables, or create a .env file at your project root:
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```bash
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OPENAI_API_KEY=sk-...
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OPENAI_CHAT_MODEL_ID=...
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OPENAI_RESPONSES_MODEL_ID=...
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...
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AZURE_OPENAI_API_KEY=...
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AZURE_OPENAI_ENDPOINT=...
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AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=...
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...
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AZURE_AI_PROJECT_ENDPOINT=...
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AZURE_AI_MODEL_DEPLOYMENT_NAME=...
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```
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You can also override environment variables by explicitly passing configuration parameters to the chat client constructor:
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```python
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from agent_framework.azure import AzureOpenAIChatClient
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chat_client = AzureOpenAIChatClient(
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api_key="",
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endpoint="",
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deployment_name="",
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api_version="",
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)
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```
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See the following [setup guide](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started) for more information.
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## 2. Create a Simple Agent
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Create agents and invoke them directly:
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```python
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import asyncio
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from agent_framework import ChatAgent
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from agent_framework.openai import OpenAIChatClient
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async def main():
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agent = ChatAgent(
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chat_client=OpenAIChatClient(),
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instructions="""
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1) A robot may not injure a human being...
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2) A robot must obey orders given it by human beings...
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3) A robot must protect its own existence...
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Give me the TLDR in exactly 5 words.
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"""
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)
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result = await agent.run("Summarize the Three Laws of Robotics")
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print(result)
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asyncio.run(main())
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# Output: Protect humans, obey, self-preserve, prioritized.
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```
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## 3. Directly Use Chat Clients (No Agent Required)
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You can use the chat client classes directly for advanced workflows:
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```python
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import asyncio
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from agent_framework.openai import OpenAIChatClient
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from agent_framework import ChatMessage, Role
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async def main():
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client = OpenAIChatClient()
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messages = [
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ChatMessage(role=Role.SYSTEM, text="You are a helpful assistant."),
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ChatMessage(role=Role.USER, text="Write a haiku about Agent Framework.")
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]
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response = await client.get_response(messages)
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print(response.messages[0].text)
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"""
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Output:
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Agents work in sync,
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Framework threads through each task—
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Code sparks collaboration.
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"""
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asyncio.run(main())
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```
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## 4. Build an Agent with Tools and Functions
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Enhance your agent with custom tools and function calling:
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```python
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import asyncio
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from typing import Annotated
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from random import randint
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from pydantic import Field
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from agent_framework import ChatAgent
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from agent_framework.openai import OpenAIChatClient
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def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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) -> str:
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"""Get the weather for a given location."""
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conditions = ["sunny", "cloudy", "rainy", "stormy"]
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return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
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def get_menu_specials() -> str:
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"""Get today's menu specials."""
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return """
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Special Soup: Clam Chowder
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Special Salad: Cobb Salad
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Special Drink: Chai Tea
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"""
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async def main():
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agent = ChatAgent(
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chat_client=OpenAIChatClient(),
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instructions="You are a helpful assistant that can provide weather and restaurant information.",
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tools=[get_weather, get_menu_specials]
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)
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response = await agent.run("What's the weather in Amsterdam and what are today's specials?")
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print(response)
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# Output:
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# The weather in Amsterdam is sunny with a high of 22°C. Today's specials include
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# Clam Chowder soup, Cobb Salad, and Chai Tea as the special drink.
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asyncio.run(main())
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```
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You can explore additional agent samples [here](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/agents).
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## 5. Multi-Agent Orchestration
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Coordinate multiple agents to collaborate on complex tasks using orchestration patterns:
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```python
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import asyncio
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from agent_framework import ChatAgent
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from agent_framework.openai import OpenAIChatClient
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async def main():
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# Create specialized agents
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writer = ChatAgent(
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chat_client=OpenAIChatClient(),
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name="Writer",
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instructions="You are a creative content writer. Generate and refine slogans based on feedback."
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)
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reviewer = ChatAgent(
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chat_client=OpenAIChatClient(),
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name="Reviewer",
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instructions="You are a critical reviewer. Provide detailed feedback on proposed slogans."
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)
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# Sequential workflow: Writer creates, Reviewer provides feedback
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task = "Create a slogan for a new electric SUV that is affordable and fun to drive."
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# Step 1: Writer creates initial slogan
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initial_result = await writer.run(task)
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print(f"Writer: {initial_result}")
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# Step 2: Reviewer provides feedback
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feedback_request = f"Please review this slogan: {initial_result}"
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feedback = await reviewer.run(feedback_request)
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print(f"Reviewer: {feedback}")
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# Step 3: Writer refines based on feedback
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refinement_request = f"Please refine this slogan based on the feedback: {initial_result}\nFeedback: {feedback}"
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final_result = await writer.run(refinement_request)
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print(f"Final Slogan: {final_result}")
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# Example Output:
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# Writer: "Charge Forward: Affordable Adventure Awaits!"
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# Reviewer: "Good energy, but 'Charge Forward' is overused in EV marketing..."
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# Final Slogan: "Power Up Your Adventure: Premium Feel, Smart Price!"
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if __name__ == "__main__":
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asyncio.run(main())
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```
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**Note**: Advanced orchestration patterns like GroupChat, Sequential, and Concurrent orchestrations are coming soon.
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## More Examples & Samples
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- [Getting Started with Agents](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/agents): Basic agent creation and tool usage
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- [Chat Client Examples](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/chat_client): Direct chat client usage patterns
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- [Azure AI Integration](https://github.com/microsoft/agent-framework/tree/main/python/packages/azure-ai): Azure AI integration
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- [.NET Orchestration Samples](https://github.com/microsoft/agent-framework/tree/main/dotnet/samples/GettingStarted/Orchestration): Advanced multi-agent patterns (.NET)
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## Agent Framework Documentation
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- [Agent Framework Repository](https://github.com/microsoft/agent-framework)
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- [Python Package Documentation](https://github.com/microsoft/agent-framework/tree/main/python)
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- [.NET Package Documentation](https://github.com/microsoft/agent-framework/tree/main/dotnet)
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- [Design Documents](https://github.com/microsoft/agent-framework/tree/main/docs/design)
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- Learn docs are coming soon.
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